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The Path to Consensus on Artificial Intelligence Assurance

Published

Author(s)

Laura Freeman, Feras Batarseh, D. Richard Kuhn, M S Raunak, Raghu N. Kacker

Abstract

Widescale adoption of intelligent algorithms requires that Artificial Intelligence (AI) engineers provide assurances that an algorithm will perform as intended. Providing such guarantees involves quantifying capabilities and the associated risks across multiple dimensions including: data quality, algorithm performance, statistical considerations, trustworthiness, security, as well as explainability. In this article we suggest a path forward for the formalization of AI assurance, including its key components.
Citation
Computer (IEEE Computer)
Volume
55
Issue
3

Keywords

artificial intelligence, data quality, software testing, statistics

Citation

Freeman, L. , Batarseh, F. , Kuhn, D. , Raunak, M. and Kacker, R. (2022), The Path to Consensus on Artificial Intelligence Assurance, Computer (IEEE Computer), [online], https://doi.org/10.1109/MC.2021.3129027, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=932907 (Accessed May 26, 2024)

Issues

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Created March 15, 2022, Updated April 18, 2024